class_docstr: Pre-processor for Hmbd51 dataset mentioned here -
      https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/
  
  This dataset consists of classwise folds with each class consisting of 3
      folds (splits).
  
  The videos directory is of the format,
      video_dir_path/class_x/<somevideo_name>.avi
      ...
      video_dir_path/class_y/<somevideo_name>.avi
  
  The splits/fold directory is of the format,
      folds_dir_path/class_x_test_split_1.txt
      folds_dir_path/class_x_test_split_2.txt
      folds_dir_path/class_x_test_split_3.txt
      ...
      folds_dir_path/class_y_test_split_1.txt
      folds_dir_path/class_y_test_split_2.txt
      folds_dir_path/class_y_test_split_3.txt
  
  And each text file in the splits directory class_x_test_split_<1 or 2 or 3>.txt
      <a video as in video_dir_path/class_x> <0 or 1 or 2>
      where 0,1,2 corresponds to unused, train split respectively.
  
  Each video has name of format
      <some_name>_<tag1>_<tag2>_<tag_3>_<tag4>_<tag5>_<some_id>.avi
  For more details on tags -
      https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/
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code: def path_prefix(self, prefix):
      self._path_prefix = prefix
  
file_path: pytorchvideo/pytorchvideo/data/hmdb51.py
imports: from __future__ import annotations
  import logging
  import os
  import pathlib
  from typing import Any, Callable, List, Optional, Tuple, Type, Union
  import torch
  import torch.utils.data
  from iopath.common.file_io import g_pathmgr
  from .clip_sampling import ClipSampler
  from .labeled_video_dataset import LabeledVideoDataset
label_desc: Use image node_id 4 for calling the Hmdb51LabeledVideoPaths obj's underlying member method code with example usage: obj.path_prefix(prefix) without return types